{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T23:19:07Z","timestamp":1783034347621,"version":"3.54.6"},"reference-count":82,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100007957","name":"Chongqing Municipal Education Commission","doi-asserted-by":"publisher","award":["HZ 2021015"],"award-info":[{"award-number":["HZ 2021015"]}],"id":[{"id":"10.13039\/501100007957","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Signal Processing: Image Communication"],"published-print":{"date-parts":[[2026,10]]},"DOI":"10.1016\/j.image.2026.117634","type":"journal-article","created":{"date-parts":[[2026,6,12]],"date-time":"2026-06-12T00:15:30Z","timestamp":1781223330000},"page":"117634","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["ED-Former: Efficient dehazing transformer with Attention-Adaptive Feed-Forward Network"],"prefix":"10.1016","volume":"148","author":[{"given":"Jinrong","family":"Chen","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yulin","family":"He","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xin","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhongyuan","family":"Guo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jingtong","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhe","family":"Rao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yi","family":"Xiang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"9","key":"10.1016\/j.image.2026.117634_b1","doi-asserted-by":"crossref","first-page":"973","DOI":"10.1007\/s11263-018-1072-8","article-title":"Semantic foggy scene understanding with synthetic data","volume":"126","author":"Sakaridis","year":"2018","journal-title":"Int. J. Comput. Vis."},{"issue":"7","key":"10.1016\/j.image.2026.117634_b2","first-page":"8284","article-title":"Detection-friendly dehazing: Object detection in real-world hazy scenes","volume":"45","author":"Li","year":"2023","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"2","key":"10.1016\/j.image.2026.117634_b3","doi-asserted-by":"crossref","first-page":"303","DOI":"10.3390\/ai3020019","article-title":"Detection in adverse weather conditions for autonomous vehicles via deep learning","volume":"3","author":"Al-Haija","year":"2022","journal-title":"Ai"},{"issue":"3","key":"10.1016\/j.image.2026.117634_b4","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1023\/A:1016328200723","article-title":"Vision and the atmosphere","volume":"48","author":"Narasimhan","year":"2002","journal-title":"Int. J. Comput. Vis."},{"issue":"12","key":"10.1016\/j.image.2026.117634_b5","first-page":"2341","article-title":"Single image haze removal using dark channel prior","volume":"33","author":"He","year":"2010","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.image.2026.117634_b6","series-title":"2008 IEEE Conference on Computer Vision and Pattern Recognition","first-page":"1","article-title":"Visibility in bad weather from a single image","author":"Tan","year":"2008"},{"key":"10.1016\/j.image.2026.117634_b7","series-title":"2009 IEEE 12th International Conference on Computer Vision","first-page":"2201","article-title":"Fast visibility restoration from a single color or gray level image","author":"Tarel","year":"2009"},{"issue":"1","key":"10.1016\/j.image.2026.117634_b8","doi-asserted-by":"crossref","first-page":"492","DOI":"10.1109\/TIP.2018.2867951","article-title":"Benchmarking single-image dehazing and beyond","volume":"28","author":"Li","year":"2018","journal-title":"IEEE Trans. Image Process."},{"issue":"11","key":"10.1016\/j.image.2026.117634_b9","doi-asserted-by":"crossref","first-page":"5187","DOI":"10.1109\/TIP.2016.2598681","article-title":"Dehazenet: An end-to-end system for single image haze removal","volume":"25","author":"Cai","year":"2016","journal-title":"IEEE Trans. Image Process."},{"key":"10.1016\/j.image.2026.117634_b10","series-title":"European Conference on Computer Vision","first-page":"154","article-title":"Single image dehazing via multi-scale convolutional neural networks","author":"Ren","year":"2016"},{"key":"10.1016\/j.image.2026.117634_b11","doi-asserted-by":"crossref","unstructured":"H. Zhang, V.M. Patel, Densely connected pyramid dehazing network, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018, pp. 3194\u20133203.","DOI":"10.1109\/CVPR.2018.00337"},{"key":"10.1016\/j.image.2026.117634_b12","doi-asserted-by":"crossref","unstructured":"B. Li, X. Peng, Z. Wang, J. Xu, D. Feng, Aod-net: All-in-one dehazing network, in: Proceedings of the IEEE International Conference on Computer Vision, 2017, pp. 4770\u20134778.","DOI":"10.1109\/ICCV.2017.511"},{"key":"10.1016\/j.image.2026.117634_b13","doi-asserted-by":"crossref","DOI":"10.1049\/cit2.70011","article-title":"WaveLiteDehaze-network: A low-parameter wavelet-based method for real-time dehazing","author":"Murtaza","year":"2025","journal-title":"CAAI Trans. Intell. Technol."},{"key":"10.1016\/j.image.2026.117634_b14","series-title":"European Conference on Computer Vision","first-page":"188","article-title":"Physics-based feature dehazing networks","author":"Dong","year":"2020"},{"key":"10.1016\/j.image.2026.117634_b15","series-title":"European Conference on Computer Vision","first-page":"722","article-title":"Hardgan: A haze-aware representation distillation gan for single image dehazing","author":"Deng","year":"2020"},{"key":"10.1016\/j.image.2026.117634_b16","doi-asserted-by":"crossref","unstructured":"H. Dong, J. Pan, L. Xiang, Z. Hu, X. Zhang, F. Wang, M.-H. Yang, Multi-scale boosted dehazing network with dense feature fusion, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2020, pp. 2157\u20132167.","DOI":"10.1109\/CVPR42600.2020.00223"},{"key":"10.1016\/j.image.2026.117634_b17","doi-asserted-by":"crossref","unstructured":"H. Wu, Y. Qu, S. Lin, J. Zhou, R. Qiao, Z. Zhang, Y. Xie, L. Ma, Contrastive learning for compact single image dehazing, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2021, pp. 10551\u201310560.","DOI":"10.1109\/CVPR46437.2021.01041"},{"key":"10.1016\/j.image.2026.117634_b18","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2021.107279","article-title":"EAA-net: A novel edge assisted attention network for single image dehazing","volume":"228","author":"Wang","year":"2021","journal-title":"Knowl.-Based Syst."},{"key":"10.1016\/j.image.2026.117634_b19","doi-asserted-by":"crossref","first-page":"8968","DOI":"10.1109\/TIP.2021.3116790","article-title":"Light-DehazeNet: a novel lightweight CNN architecture for single image dehazing","volume":"30","author":"Ullah","year":"2021","journal-title":"IEEE Trans. Image Process."},{"key":"10.1016\/j.image.2026.117634_b20","doi-asserted-by":"crossref","unstructured":"X. Qin, Z. Wang, Y. Bai, X. Xie, H. Jia, FFA-Net: Feature fusion attention network for single image dehazing, in: Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 34, 2020, pp. 11908\u201311915.","DOI":"10.1609\/aaai.v34i07.6865"},{"key":"10.1016\/j.image.2026.117634_b21","series-title":"2019 IEEE Winter Conference on Applications of Computer Vision","first-page":"1375","article-title":"Gated context aggregation network for image dehazing and deraining","author":"Chen","year":"2019"},{"key":"10.1016\/j.image.2026.117634_b22","doi-asserted-by":"crossref","unstructured":"X. Liu, Y. Ma, Z. Shi, J. Chen, Griddehazenet: Attention-based multi-scale network for image dehazing, in: Proceedings of the IEEE\/CVF International Conference on Computer Vision, 2019, pp. 7314\u20137323.","DOI":"10.1109\/ICCV.2019.00741"},{"key":"10.1016\/j.image.2026.117634_b23","series-title":"WaveDH: Wavelet sub-bands guided convnet for efficient image dehazing","author":"Hwang","year":"2024"},{"key":"10.1016\/j.image.2026.117634_b24","doi-asserted-by":"crossref","unstructured":"W. Ren, L. Ma, J. Zhang, J. Pan, X. Cao, W. Liu, M.-H. Yang, Gated fusion network for single image dehazing, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018, pp. 3253\u20133261.","DOI":"10.1109\/CVPR.2018.00343"},{"issue":"15","key":"10.1016\/j.image.2026.117634_b25","doi-asserted-by":"crossref","first-page":"17449","DOI":"10.1007\/s10489-022-03157-4","article-title":"Gated residual feature attention network for real-time dehazing","volume":"52","author":"Yi","year":"2022","journal-title":"Appl. Intell."},{"issue":"4","key":"10.1016\/j.image.2026.117634_b26","doi-asserted-by":"crossref","first-page":"2293","DOI":"10.1007\/s00371-023-02917-8","article-title":"MFAF-net: image dehazing with multi-level features and adaptive fusion","volume":"40","author":"Yi","year":"2024","journal-title":"Vis. Comput."},{"key":"10.1016\/j.image.2026.117634_b27","doi-asserted-by":"crossref","DOI":"10.1016\/j.neunet.2024.106165","article-title":"Priors-assisted dehazing network with attention supervision and detail preservation","volume":"173","author":"Yi","year":"2024","journal-title":"Neural Netw."},{"key":"10.1016\/j.image.2026.117634_b28","series-title":"An image is worth 16x16 words: Transformers for image recognition at scale","author":"Dosovitskiy","year":"2020"},{"key":"10.1016\/j.image.2026.117634_b29","doi-asserted-by":"crossref","unstructured":"Z. Liu, Y. Lin, Y. Cao, H. Hu, Y. Wei, Z. Zhang, S. Lin, B. Guo, Swin transformer: Hierarchical vision transformer using shifted windows, in: Proceedings of the IEEE\/CVF International Conference on Computer Vision, 2021, pp. 10012\u201310022.","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"10.1016\/j.image.2026.117634_b30","doi-asserted-by":"crossref","unstructured":"W. Wang, E. Xie, X. Li, D.-P. Fan, K. Song, D. Liang, T. Lu, P. Luo, L. Shao, Pyramid vision transformer: A versatile backbone for dense prediction without convolutions, in: Proceedings of the IEEE\/CVF International Conference on Computer Vision, 2021, pp. 568\u2013578.","DOI":"10.1109\/ICCV48922.2021.00061"},{"key":"10.1016\/j.image.2026.117634_b31","doi-asserted-by":"crossref","unstructured":"L. Yuan, Y. Chen, T. Wang, W. Yu, Y. Shi, Z.-H. Jiang, F.E. Tay, J. Feng, S. Yan, Tokens-to-token vit: Training vision transformers from scratch on imagenet, in: Proceedings of the IEEE\/CVF International Conference on Computer Vision, 2021, pp. 558\u2013567.","DOI":"10.1109\/ICCV48922.2021.00060"},{"key":"10.1016\/j.image.2026.117634_b32","first-page":"3965","article-title":"Coatnet: Marrying convolution and attention for all data sizes","volume":"34","author":"Dai","year":"2021","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.image.2026.117634_b33","doi-asserted-by":"crossref","unstructured":"C.-F.R. Chen, Q. Fan, R. Panda, Crossvit: Cross-attention multi-scale vision transformer for image classification, in: Proceedings of the IEEE\/CVF International Conference on Computer Vision, 2021, pp. 357\u2013366.","DOI":"10.1109\/ICCV48922.2021.00041"},{"key":"10.1016\/j.image.2026.117634_b34","doi-asserted-by":"crossref","unstructured":"X. Dong, J. Bao, D. Chen, W. Zhang, N. Yu, L. Yuan, D. Chen, B. Guo, Cswin transformer: A general vision transformer backbone with cross-shaped windows, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2022, pp. 12124\u201312134.","DOI":"10.1109\/CVPR52688.2022.01181"},{"key":"10.1016\/j.image.2026.117634_b35","series-title":"How do vision transformers work?","author":"Park","year":"2022"},{"key":"10.1016\/j.image.2026.117634_b36","series-title":"Rethinking attention with performers","author":"Choromanski","year":"2020"},{"key":"10.1016\/j.image.2026.117634_b37","series-title":"Uniformer: Unified transformer for efficient spatiotemporal representation learning","author":"Li","year":"2022"},{"key":"10.1016\/j.image.2026.117634_b38","first-page":"30008","article-title":"Focal attention for long-range interactions in vision transformers","volume":"34","author":"Yang","year":"2021","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.image.2026.117634_b39","series-title":"Mobilenets: Efficient convolutional neural networks for mobile vision applications","author":"Howard","year":"2017"},{"key":"10.1016\/j.image.2026.117634_b40","series-title":"Mobilevit: light-weight, general-purpose, and mobile-friendly vision transformer","author":"Mehta","year":"2021"},{"key":"10.1016\/j.image.2026.117634_b41","doi-asserted-by":"crossref","unstructured":"J. Guo, K. Han, H. Wu, Y. Tang, X. Chen, Y. Wang, C. Xu, Cmt: Convolutional neural networks meet vision transformers, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2022, pp. 12175\u201312185.","DOI":"10.1109\/CVPR52688.2022.01186"},{"key":"10.1016\/j.image.2026.117634_b42","doi-asserted-by":"crossref","unstructured":"A. Kirillov, E. Mintun, N. Ravi, H. Mao, C. Rolland, L. Gustafson, T. Xiao, S. Whitehead, A.C. Berg, W.-Y. Lo, et al., Segment anything, in: Proceedings of the IEEE\/CVF International Conference on Computer Vision, 2023, pp. 4015\u20134026.","DOI":"10.1109\/ICCV51070.2023.00371"},{"key":"10.1016\/j.image.2026.117634_b43","doi-asserted-by":"crossref","first-page":"1927","DOI":"10.1109\/TIP.2023.3256763","article-title":"Vision transformers for single image dehazing","volume":"32","author":"Song","year":"2023","journal-title":"IEEE Trans. Image Process."},{"key":"10.1016\/j.image.2026.117634_b44","doi-asserted-by":"crossref","unstructured":"Z. Chen, J. Wu, W. Wang, W. Su, G. Chen, S. Xing, M. Zhong, Q. Zhang, X. Zhu, L. Lu, et al., Internvl: Scaling up vision foundation models and aligning for generic visual-linguistic tasks, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2024, pp. 24185\u201324198.","DOI":"10.1109\/CVPR52733.2024.02283"},{"key":"10.1016\/j.image.2026.117634_b45","series-title":"Llavanext: Improved reasoning, ocr, and world knowledge","author":"Liu","year":"2024"},{"key":"10.1016\/j.image.2026.117634_b46","series-title":"State space model for new-generation network alternative to transformers: A survey","author":"Wang","year":"2024"},{"key":"10.1016\/j.image.2026.117634_b47","doi-asserted-by":"crossref","unstructured":"N. Ma, X. Zhang, H.-T. Zheng, J. Sun, Shufflenet v2: Practical guidelines for efficient cnn architecture design, in: Proceedings of the European Conference on Computer Vision, ECCV, 2018, pp. 116\u2013131.","DOI":"10.1007\/978-3-030-01264-9_8"},{"key":"10.1016\/j.image.2026.117634_b48","series-title":"SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and < 0.5 MB model size","author":"Iandola","year":"2016"},{"key":"10.1016\/j.image.2026.117634_b49","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1109\/TIP.2019.2922837","article-title":"FAMED-net: A fast and accurate multi-scale end-to-end dehazing network","volume":"29","author":"Zhang","year":"2019","journal-title":"IEEE Trans. Image Process."},{"key":"10.1016\/j.image.2026.117634_b50","doi-asserted-by":"crossref","DOI":"10.1016\/j.displa.2023.102577","article-title":"Frequency-guidance collaborative triple-branch network for single image dehazing","volume":"80","author":"Yi","year":"2023","journal-title":"Displays"},{"key":"10.1016\/j.image.2026.117634_b51","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2024.111074","article-title":"Eenet: An effective and efficient network for single image dehazing","volume":"158","author":"Cui","year":"2025","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.image.2026.117634_b52","doi-asserted-by":"crossref","unstructured":"X. Su, S. Li, Y. Cui, M. Cao, Y. Zhang, Z. Chen, Z. Wu, Z. Wang, Y. Zhang, X. Yuan, Prior-guided hierarchical harmonization network for efficient image dehazing, in: Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 39, 2025, pp. 7042\u20137050.","DOI":"10.1609\/aaai.v39i7.32756"},{"key":"10.1016\/j.image.2026.117634_b53","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2023.121130","article-title":"Towards compact single image dehazing via task-related contrastive network","volume":"235","author":"Yi","year":"2024","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.image.2026.117634_b54","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2025.126549","article-title":"Multi-stage dehazing network: Where haze perception unit meets global and local progressive contrastive regularization","volume":"270","author":"Yi","year":"2025","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.image.2026.117634_b55","doi-asserted-by":"crossref","DOI":"10.1016\/j.neucom.2023.126494","article-title":"Semi-supervised progressive dehazing network using unlabeled contrastive guidance","volume":"551","author":"Yi","year":"2023","journal-title":"Neurocomputing"},{"key":"10.1016\/j.image.2026.117634_b56","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2025.114279","article-title":"You only need haze: Bidirectional disentangled translation network for unsupervised image dehazing","author":"Yi","year":"2025","journal-title":"Knowl.-Based Syst."},{"key":"10.1016\/j.image.2026.117634_b57","doi-asserted-by":"crossref","DOI":"10.1016\/j.inffus.2025.103104","article-title":"Towards haze removal with derived pseudo-label supervision from real-world non-aligned training data","volume":"120","author":"Yi","year":"2025","journal-title":"Inf. Fusion"},{"key":"10.1016\/j.image.2026.117634_b58","doi-asserted-by":"crossref","DOI":"10.1109\/TNNLS.2026.3673760","article-title":"When optimal transport meets photo-realistic image dehazing with unpaired training","author":"Wen","year":"2026","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"10.1016\/j.image.2026.117634_b59","doi-asserted-by":"crossref","unstructured":"H. Wu, B. Xiao, N. Codella, M. Liu, X. Dai, L. Yuan, L. Zhang, Cvt: Introducing convolutions to vision transformers, in: Proceedings of the IEEE\/CVF International Conference on Computer Vision, 2021, pp. 22\u201331.","DOI":"10.1109\/ICCV48922.2021.00009"},{"key":"10.1016\/j.image.2026.117634_b60","series-title":"Regionvit: Regional-to-local attention for vision transformers","author":"Chen","year":"2021"},{"key":"10.1016\/j.image.2026.117634_b61","series-title":"International Conference on Machine Learning","first-page":"10347","article-title":"Training data-efficient image transformers & distillation through attention","author":"Touvron","year":"2021"},{"key":"10.1016\/j.image.2026.117634_b62","doi-asserted-by":"crossref","unstructured":"K. He, X. Chen, S. Xie, Y. Li, P. Doll\u00e1r, R. Girshick, Masked autoencoders are scalable vision learners, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2022, pp. 16000\u201316009.","DOI":"10.1109\/CVPR52688.2022.01553"},{"key":"10.1016\/j.image.2026.117634_b63","series-title":"Beit: Bert pre-training of image transformers","author":"Bao","year":"2021"},{"key":"10.1016\/j.image.2026.117634_b64","doi-asserted-by":"crossref","unstructured":"H. Fan, B. Xiong, K. Mangalam, Y. Li, Z. Yan, J. Malik, C. Feichtenhofer, Multiscale vision transformers, in: Proceedings of the IEEE\/CVF International Conference on Computer Vision, 2021, pp. 6824\u20136835.","DOI":"10.1109\/ICCV48922.2021.00675"},{"key":"10.1016\/j.image.2026.117634_b65","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2023.111156","article-title":"Exploring the potential of channel interactions for image restoration","volume":"282","author":"Cui","year":"2023","journal-title":"Knowl.-Based Syst."},{"key":"10.1016\/j.image.2026.117634_b66","doi-asserted-by":"crossref","unstructured":"Y. Cui, W. Ren, A. Knoll, Omni-kernel network for image restoration, in: Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 38, 2024, pp. 1426\u20131434.","DOI":"10.1609\/aaai.v38i2.27907"},{"key":"10.1016\/j.image.2026.117634_b67","doi-asserted-by":"crossref","DOI":"10.1109\/TMM.2025.3535316","article-title":"All-in-one weather-degraded image restoration via adaptive degradation-aware self-prompting model","author":"Wen","year":"2025","journal-title":"IEEE Trans. Multimed."},{"key":"10.1016\/j.image.2026.117634_b68","article-title":"Structure-preserving frequency-regularized text-guided optimal transport for unpaired rain streaks and raindrops removal","author":"Wen","year":"2026","journal-title":"IEEE Trans. Multimed."},{"key":"10.1016\/j.image.2026.117634_b69","series-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention","first-page":"234","article-title":"U-net: Convolutional networks for biomedical image segmentation","author":"Ronneberger","year":"2015"},{"key":"10.1016\/j.image.2026.117634_b70","doi-asserted-by":"crossref","unstructured":"X. Li, W. Wang, X. Hu, J. Yang, Selective kernel networks, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2019, pp. 510\u2013519.","DOI":"10.1109\/CVPR.2019.00060"},{"key":"10.1016\/j.image.2026.117634_b71","series-title":"European Conference on Computer Vision","first-page":"694","article-title":"Perceptual losses for real-time style transfer and super-resolution","author":"Johnson","year":"2016"},{"key":"10.1016\/j.image.2026.117634_b72","doi-asserted-by":"crossref","unstructured":"J. Hu, L. Shen, G. Sun, Squeeze-and-excitation networks, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018, pp. 7132\u20137141.","DOI":"10.1109\/CVPR.2018.00745"},{"key":"10.1016\/j.image.2026.117634_b73","doi-asserted-by":"crossref","unstructured":"S.W. Zamir, A. Arora, S. Khan, M. Hayat, F.S. Khan, M.-H. Yang, Restormer: Efficient transformer for high-resolution image restoration, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2022, pp. 5728\u20135739.","DOI":"10.1109\/CVPR52688.2022.00564"},{"key":"10.1016\/j.image.2026.117634_b74","series-title":"International Conference on Machine Learning","first-page":"7324","article-title":"Making convolutional networks shift-invariant again","author":"Zhang","year":"2019"},{"key":"10.1016\/j.image.2026.117634_b75","doi-asserted-by":"crossref","unstructured":"P. Liu, H. Zhang, K. Zhang, L. Lin, W. Zuo, Multi-level wavelet-CNN for image restoration, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2018, pp. 773\u2013782.","DOI":"10.1109\/CVPRW.2018.00121"},{"key":"10.1016\/j.image.2026.117634_b76","series-title":"A Wavelet Tour of Signal Processing","author":"Mallat","year":"1999"},{"key":"10.1016\/j.image.2026.117634_b77","doi-asserted-by":"crossref","unstructured":"C. Ledig, L. Theis, F. Husz\u00e1r, J. Caballero, A. Cunningham, A. Acosta, A. Aitken, A. Tejani, J. Totz, Z. Wang, et al., Photo-realistic single image super-resolution using a generative adversarial network, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017, pp. 4681\u20134690.","DOI":"10.1109\/CVPR.2017.19"},{"key":"10.1016\/j.image.2026.117634_b78","series-title":"Sgdr: Stochastic gradient descent with warm restarts","author":"Loshchilov","year":"2016"},{"key":"10.1016\/j.image.2026.117634_b79","doi-asserted-by":"crossref","unstructured":"C.-L. Guo, Q. Yan, S. Anwar, R. Cong, W. Ren, C. Li, Image dehazing transformer with transmission-aware 3d position embedding, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2022, pp. 5812\u20135820.","DOI":"10.1109\/CVPR52688.2022.00572"},{"key":"10.1016\/j.image.2026.117634_b80","doi-asserted-by":"crossref","unstructured":"C.O. Ancuti, C. Ancuti, R. Timofte, C. De Vleeschouwer, O-haze: a dehazing benchmark with real hazy and haze-free outdoor images, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2018, pp. 754\u2013762.","DOI":"10.1109\/CVPRW.2018.00119"},{"key":"10.1016\/j.image.2026.117634_b81","series-title":"U-shaped vision mamba for single image dehazing","author":"Zheng","year":"2024"},{"key":"10.1016\/j.image.2026.117634_b82","doi-asserted-by":"crossref","unstructured":"Y.-C. Lin, Y.-S. Xu, H.-W. Chen, H.-K. Kuo, C.-Y. Lee, Eamamba: Efficient all-around vision state space model for image restoration, in: Proceedings of the IEEE\/CVF International Conference on Computer Vision, 2025, pp. 11708\u201311719.","DOI":"10.1109\/ICCV51701.2025.01089"}],"container-title":["Signal Processing: Image Communication"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0923596526001578?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0923596526001578?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T22:49:02Z","timestamp":1783032542000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0923596526001578"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,10]]},"references-count":82,"alternative-id":["S0923596526001578"],"URL":"https:\/\/doi.org\/10.1016\/j.image.2026.117634","relation":{},"ISSN":["0923-5965"],"issn-type":[{"value":"0923-5965","type":"print"}],"subject":[],"published":{"date-parts":[[2026,10]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"ED-Former: Efficient dehazing transformer with Attention-Adaptive Feed-Forward Network","name":"articletitle","label":"Article Title"},{"value":"Signal Processing: Image Communication","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.image.2026.117634","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"117634"}}